ml-cur

ML-Cur Implementation

This is our implementation for ML-Cur, a curriculum based approach for fitting distributions. It was introduced in our work Curriculum-Based Imitation of Versatile Skills.

Installation

You can install our package by downloading this repository and calling: pip install <path-to-ml-cur>

Usage

Our Public Interfaces follow a structure inspired by scikit-learn. See also the IPython Notebooks in our demo folder.

from ml_cur import MlCurLinMoe
ml_cur_moe = MlCurLinMoe(n_components=2, train_iter=50, num_active_samples=0.4)
ml_cur_moe.fit(train_samples, train_contexts)

If you find our work useful, please consider citing:

@INPROCEEDINGS{Li2023Curriculum,
  author = {Li, Maximilian Xiling 
            and Celik, Onur 
            and Becker, Philipp 
            and Blessing, Denis 
            and Lioutikov, Rudolf 
            and Neumann, Gerhard},
  title  = {Curriculum-Based Imitation of Versatile Skills},
  booktitle={2023 International Conference on Robotics and Automation (ICRA)},
  year   = {2023},
}